Information Conductivity:Universal Performance Measure for Semantic Communications

As a novel paradigm,semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems.However,it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method.In this paper,we first present a review of the semantic communication system,including its system model and the two typical coding and transmission methods for its implementations.To address the unsolved issue of the information transmission capability measure for semantic communication methods,we propose a new universal performance measure called Information Conductivity.We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods,degrees of freedom,and progressive analysis.Experimental results in image transmission scenarios validate its practical applicability.

Traffic Allocation Scheme with Cooperation of Multiple RANs in Universal Wireless Environments

This paper introduces an adaptive traffic allocation scheme with cooperation of multiple Radio Access Networks (RANs) in universal wireless environments.The different cooperation scenarios are studied,and based on the scenario of cooperation in both network layer and terminal layer,an open queuing system model,which is aiming to depict the characteristics of packet loss rate of wireless communication networks,is proposed to optimize the traffic allocation results.The analysis and numerical simulations indicate that the proposed scheme achieves inter-networking load balance tominimize the whole transmission delay and expands the communication ability of single-mode terminals to support high data rate traffics.

MDTCNet:Multi-Task Classifications Network and TCNN for Direction of Arrival Estimation

The direction-of-arrival(DoA) estimation is one of the hot research areas in signal processing. To overcome the DoA estimation challenge without the prior information about signal sources number and multipath number in millimeter wave system,the multi-task deep residual shrinkage network(MTDRSN) and transfer learning-based convolutional neural network(TCNN), namely MDTCNet, are proposed. The sampling covariance matrix based on the received signal is used as the input to the proposed network. A DRSN-based multi-task classifications model is first introduced to estimate signal sources number and multipath number simultaneously. Then, the DoAs with multi-signal and multipath are estimated by the regression model. The proposed CNN is applied for DoAs estimation with the predicted number of signal sources and paths. Furthermore, the modelbased transfer learning is also introduced into the regression model. The TCNN inherits the partial network parameters of the already formed optimization model obtained by the CNN. A series of experimental results show that the MDTCNet-based DoAs estimation method can accurately predict the signal sources number and multipath number under a range of signal-to-noise ratios. Remarkably, the proposed method achieves the lower root mean square error compared with some existing deep learning-based and traditional methods.

Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

Random Access and Resource Allocation for the Coexistence of NOMA-Based and OMA-Based M2M Communications

In the future fifth generation(5G) systems,non-orthogonal multiple access(NOMA) is a promising technology that can greatly enhance the network capacity compared to orthogonal multiple access(OMA) .In this paper,we propose a novel random access(RA) and resource allocation scheme for the coexistence of NOMA-based and OMAbased machine-to-machine(M2M) communications,which aims at improving the number of successful data packet transmissions and guaranteeing the quality of service(Qo S) (e.g.,the minimum data rate requirement) for M2 M communications.The algorithm of joint user equipment(UE) paring and power allocation is proposed for the coexisting RA(i.e.,the coexistence of NOMA-based RA and OMA-based RA) .The resource allocation for the coexisting RA is investigated,thus improving the number of successful data packet transmissions by more efficiently using the radio resources.Simulation results demonstrate that the proposed RA and resource allocation scheme outperforms the conventional RA in terms of the number of successful data packet transmissions,thus is a promising technology in future M2 M communications.

Asymptotic Analysis of Transmission Capacities for Overlaid Spread-Spectrum Wireless Networks

We study the transmission capacities of two coexisting spread-spectrum wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. We defi ne transmission capacity as the product among the density of transmissions, the transmission rate, and the successful transmission probability. The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, while the SR network limits its interference to the PR network by carefully controlling the density ofits transmitters. Considering two types of spread-spectrum transmission schemes (FH-CDMA and DS-CDMA) and the channel inversion power control mechanism, we quantify the transmission capacities for these two networks based on asymptotic analysis. Our results show that if the PR network permits a small increase ofits outage probability, the sum transmission capacities of the two networks (i.e., the overall spectrumefficiency per unit area) will be boosted significantly over that of a single network.

A Golden Decade of Polar Codes:From Basic Principle to 5G Applications

After the pursuit of seventy years,the invention of polar codes indicates that we have found the first capacity-achieving coding with low complexity construction and decoding,which is the great breakthrough of the coding theory in the past two decades.In this survey,we retrospect the history of polar codes and summarize the advancement in the past ten years.First,the primary principle of channel polarization is investigated such that the basic construction,coding method and the classic successive cancellation(SC)decoding are reviewed.Second,in order to improve the performance of the finite code length,we introduce the guiding principle and conclude five design criteria for the construction,design and implementation of the polar code in the practical communication system based on the exemplar schemes in the literature.Especially,we explain the design principle behind the concatenated coding and rate matching of polar codes in 5G wireless system.Furthermore,the improved SC decoding algorithms,such as SC list(SCL)decoding and SC stack(SCS)decoding etc.,are investigated and compared.Finally,the research prospects of polar codes for the future 6G communication system are explored,including the optimization of short polar codes,coding construction in fading channels,polar coded modulation and HARQ,and the polar coded transmission,namely polar processing.Predictably,as a new coding methodology,polar codes will shine a light on communication theory and unveil a revolution in transmission technology.

RIS Assisted Dual-Function Radar and Secure Communications Based on Frequency-Shifted Chirp Spread Spectrum Index Modulation

Reconfigurable intelligent surface(RIS)assisted dual-function radar communications(DFRC)system is a promising integrated sensing and communication(ISAC)technology for future 6G.In this paper,we propose a scheme of RIS-assisted DFRC system based on frequency shifted chirp spread spectrum index modulation(RDFI)for secure communications.The proposed RDFI achieves the sensing and transmission of target location information in its radar and communication modes,respectively.In both modes,the frequency-shifted chirp spread spectrum index modulation(FSCSS-IM)signal is used as the baseband signal for radar and communications,so that the signal sent by the radar also carries information.This scheme implements the RIS-assisted beamforming in the communication mode through the azimuth information of the target acquired in the radar mode,so that the signal received from the eavesdropper is distorted in amplitude and phase.In addition,this paper analyzes the radar measurement accuracy and communication security of the FSCSS-IM signal using ambiguity function and secrecy rate(SR)analysis,respectively.Simulation results show that RDFI achieves both excellent bit error rate(BER)performance and physical layer security of communications.

Joint Design of Content Delivery and Recommendation in Wireless Caching Networks

Although content caching and recommendation are two complementary approaches to improve the user experience,it is still challenging to provide an integrated paradigm to fully explore their potential,due to the high complexity and complicated tradeoff relationship.To provide an efficient management framework,the joint design of content delivery and recommendation in wireless content caching networks is studied in this paper.First,a joint transmission scheme of content objects and recommendation lists is designed with edge caching,and an optimization problem is formulated to balance the utility and cost of content caching and recommendation,which is an mixed integer nonlinear programming problem.Second,a reinforcement learning based algorithm is proposed to implement real time management of content caching,recommendation and delivery,which can approach the optimal solution without iterations during each decision epoch.Finally,the simulation results are provided to evaluate the performance of our proposed scheme,which show that it can achieve lower cost than the existing content caching and recommendation schemes.

Expected density of progress for wireless ad hoc networks with Nakagami-m fading

This paper presents the closed-form expression to the expected density of progress for wireless ad hoc networks with Nakagami-m fading. The expected density of progress is defined as the expectation of a product between the number of simultaneous successful transmission per unit area and the distance towards the destination. Numerical results show that the expected density of progress is determined by two factors, terminal density and the probability that a terminal attempts to transmit.

Distributed Edge Cooperation and Data Collection for Digital Twins of Wide-Areas

Digital twins for wide-areas(DT-WA)can model and predict the physical world with high fidelity by incorporating an artificial intelligence(AI)model.However,the AI model requires an energy-consuming updating process to keep pace with the dynamic environment,where studies are still in infancy.To reduce the updating energy,this paper proposes a distributed edge cooperation and data collection scheme.The AI model is partitioned into multiple sub-models deployed on different edge servers(ESs)co-located with access points across wide-area,to update distributively using local sensor data.To reduce the updating energy,ESs can choose to become either updating helpers or recipients of their neighboring ESs,based on sensor quantities and basic updating convergencies.Helpers would share their updated sub-model parameters with neighboring recipients,so as to reduce the latter updating workload.To minimize system energy under updating convergency and latency constraints,we further propose an algorithm to let ESs distributively optimize their cooperation identities,collect sensor data,and allocate wireless and computing resources.It comprises several constraint-release approaches,where two child optimization problems are solved,and designs a largescale multi-agent deep reinforcement learning algorithm.Simulation shows that the proposed scheme can efficiently reduce updating energy compared with the baselines.

System Outage Probability and Diversity Analysis of a SWIPT Based Two-Way DF Relay Network Under Transceiver Hardware Impairments

This paper investigates the system outage performance of a simultaneous wireless information and power transfer(SWIPT)based two-way decodeand-forward(DF)relay network,where potential hardware impairments(HIs)in all transceivers are considered.After harvesting energy and decoding messages simultaneously via a power splitting scheme,the energy-limited relay node forwards the decoded information to both terminals.Each terminal combines the signals from the direct and relaying links via selection combining.We derive the system outage probability under independent but non-identically distributed Nakagami-m fading channels.It reveals an overall system ceiling(OSC)effect,i.e.,the system falls in outage if the target rate exceeds an OSC threshold that is determined by the levels of HIs.Furthermore,we derive the diversity gain of the considered network.The result reveals that when the transmission rate is below the OSC threshold,the achieved diversity gain equals the sum of the shape parameter of the direct link and the smaller shape parameter of the terminalto-relay links;otherwise,the diversity gain is zero.This is different from the amplify-and-forward(AF)strategy,under which the relaying links have no contribution to the diversity gain.Simulation results validate the analytical results and reveal that compared with the AF strategy,the SWIPT based two-way relaying links under the DF strategy are more robust to HIs and achieve a lower system outage probability.

Optimization of the Internet of Remote Things Data Acquisition Based on Satellite UAV Integrated Network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things(IoRT),we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle(UAV)-low earth orbit(LEO)satellite integrated space-air-ground network,in which the UAV acquires data from massive Internet of Things(IoT)devices in special scenarios.To combine with the actual scenario,we consider two different data types,that is,delay-sensitive data and delay-tolerant data,the transmission mode is accordingly divided into two types.For delay-sensitive data,the data will be transmitted via the LEO satellite relay to the data center(DC)in real-time.For delay-tolerant data,the UAV will store and carry the data until the acquisition is completed,and then return to DC.Due to nonconvexity and complexity of the formulated problem,a multi-dimensional optimization Rate Demand based Joint Optimization(RDJO)algorithm is proposed.The algorithm first uses successive convex approximation(SCA)technology to solve the non-convexity,and then based on the block coordinate descent(BCD)method,the data acquisition efficiency is maximized by jointly optimizing UAV deployment,the bandwidth allocation of IoRT devices,and the transmission power of the UAV.Finally,the proposed RDJO algorithm is compared with the conventional algorithms.Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation,UAV deployment and the transmission power.

A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .

Variational Learned Talking-Head Semantic Coded Transmission System

Video transmission requires considerable bandwidth,and current widely employed schemes prove inadequate when confronted with scenes featuring prominently.Motivated by the strides in talkinghead generative technology,the paper introduces a semantic transmission system tailored for talking-head videos.The system captures semantic information from talking-head video and faithfully reconstructs source video at the receiver,only one-shot reference frame and compact semantic features are required for the entire transmission.Specifically,we analyze video semantics in the pixel domain frame-by-frame and jointly process multi-frame semantic information to seamlessly incorporate spatial and temporal information.Variational modeling is utilized to evaluate the diversity of importance among group semantics,thereby guiding bandwidth resource allocation for semantics to enhance system efficiency.The whole endto-end system is modeled as an optimization problem and equivalent to acquiring optimal rate-distortion performance.We evaluate our system on both reference frame and video transmission,experimental results demonstrate that our system can improve the efficiency and robustness of communications.Compared to the classical approaches,our system can save over 90%of bandwidth when user perception is close.

Co-Sharing Waveform Design for Millimeter-Wave Radar Communication Systems

Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.

Joint Beam Scheduling and Power Optimization for Beam Hopping LEO Satellite Systems

Low earth orbit(LEO) satellite communications can provide ubiquitous and reliable services,making it an essential part of the Internet of Everything network. Beam hopping(BH) is an emerging technology for effectively addressing the issue of low resource utilization caused by the non-uniform spatio-temporal distribution of traffic demands. However, how to allocate multi-dimensional resources in a timely and efficient way for the highly dynamic LEO satellite systems remains a challenge. This paper proposes a joint beam scheduling and power optimization beam hopping(JBSPO-BH) algorithm considering the differences in the geographic distribution of sink nodes. The JBSPO-BH algorithm decouples the original problem into two sub-problems. The beam scheduling problem is modelled as a potential game,and the Nash equilibrium(NE) point is obtained as the beam scheduling strategy. Moreover, the penalty function interior point method is applied to optimize the power allocation. Simulation results show that the JBSPO-BH algorithm has low time complexity and fast convergence and achieves better performance both in throughput and fairness. Compared with greedybased BH, greedy-based BH with the power optimization, round-robin BH, Max-SINR BH and satellite resource allocation algorithm, the throughput of the proposed algorithm is improved by 44.99%, 20.79%,156.06%, 15.39% and 8.17%, respectively.

Beam Training and Tracking in mmWave Communication:A Survey

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission.However,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles.To overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid beamforming.Accurately aligning the transmitter and receiver beams,i.e.beam training,is vitally important to high data rate transmission.However,it may cause huge overhead which has negative effects on initial access,handover,and tracking.Besides,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking latency.An efficient beam tracking method has a positive effect on sustaining robust links.This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile network.Especially,some open research problems are proposed to realize fast,accurate,and robust beam training and tracking.We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Reinforcement Learning-Based Joint Task Offloading and Migration Schemes Optimization in Mobility-Aware MEC Network

Intelligent edge computing carries out edge devices of the Internet of things(Io T) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of the mobility of mobile equipments(MEs), if MEs move among the reach of the small cell networks(SCNs), the offloaded tasks cannot be returned to MEs successfully. As a result, migration incurs additional costs. In this paper, joint task offloading and migration schemes in mobility-aware Mobile Edge Computing(MEC) network based on Reinforcement Learning(RL) are proposed to obtain the maximum system revenue. Firstly, the joint optimization problems of maximizing the total revenue of MEs are put forward, in view of the mobility-aware MEs. Secondly, considering time-varying computation tasks and resource conditions, the mixed integer non-linear programming(MINLP) problem is described as a Markov Decision Process(MDP). Then we propose a novel reinforcement learning-based optimization framework to work out the problem, instead traditional methods. Finally, it is shown that the proposed schemes can obviously raise the total revenue of MEs by giving simulation results.